Reverse engineering in the realm of Computer-Aided Design (CAD) has been a longstanding aspiration, though not yet entirely realized. Its primary aim is to uncover the CAD process behind a physical object given its 3D scan. We propose CAD-SIGNet, an end-to-end trainable and auto-regressive architecture to recover the design history of a CAD model represented as a sequence of sketch-and-extrusion from an input point cloud. Our model learns visual-language representations by layer-wise cross-attention between point cloud and CAD language embedding. In particular, a new Sketch instance Guided Attention (SGA) module is proposed in order to reconstruct the fine-grained details of the sketches. Thanks to its auto-regressive nature, CAD-SIGNet not only reconstructs a unique full design history of the corresponding CAD model given an input point cloud but also provides multiple plausible design choices. This allows for an interactive reverse engineering scenario by providing designers with multiple next-step choices along with the design process. Extensive experiments on publicly available CAD datasets showcase the effectiveness of our approach against existing baseline models in two settings, namely, full design history recovery and conditional auto-completion from point clouds.

翻译：在计算机辅助设计（CAD）领域的逆向工程一直是一个长期追求的目标，尽管尚未完全实现。其主要目标是从物理对象的3D扫描中揭示其背后的CAD设计过程。我们提出CAD-SIGNet，一种端到端可训练的自回归架构，用于从输入点云中恢复表示为草图-拉伸序列的CAD模型设计历史。我们的模型通过点云与CAD语言嵌入之间的逐层交叉注意力学习视觉-语言表示。特别地，我们提出了一种新的草图实例引导注意力（SGA）模块，以重建草图的细粒度细节。得益于其自回归特性，CAD-SIGNet不仅能够根据输入点云重建对应CAD模型的唯一完整设计历史，还能提供多种可行的设计选择。这使得交互式逆向工程场景成为可能，通过在设计过程中向设计者提供多个下一步选择。在公开CAD数据集上的大量实验表明，我们的方法在两种设置下（即完整设计历史恢复和基于点云的条件自动补全）均优于现有基线模型。